Principal Machine Learning Engineer, SecureAI
Core
Architecting and driving features for a unified control plane to establish an Identity Security Fabric for the agentic era, replacing static rules with dynamic AI security mechanisms.
Role type
Principal Machine Learning Engineer (Generative AI & Security)
Builds
Real-time threat inspection, agent intent evaluation, behavioral analysis, and semantic verification engines for autonomous AI agents.
Domain
Cybersecurity, Digital Identity, Generative AI, Agentic Systems
Deliverable
production ML models
Required skills
Applied machine learning, Generative AI platforms, RAG, embeddings, vector search, zero-shot classification, ML framework development, evaluation pipeline design, system architecture
Preferred skills
Identity/security product integration, ethical AI, model risk, compliance frameworks, synthetic data generation, LLM-as-a-judge methods
Technologies
Python, Go, Typescript, AWS Bedrock, OpenAI, Anthropic, LiteLLM, LangGraph, LangChain, LlamaIndex, MCP, FastAPI, PyTorch, TensorFlow, Spark ML, Airflow, Cedar
Responsibilities
Implement intent-based enforcement for agent runtime requests; Apply LLM reasoning and prompt parsing for real-time interpretation; Integrate low-latency inference engines into API gateways; Design confidence-scored decision engines; Establish evaluation benchmarks and guardrails; Architect scalable ML and GenAI systems; Optimize prompting, context retrieval, and RAG workflows; Build automated evaluation pipelines; Mentor and coach engineers
Seniority
Principal, hands-on IC with mentorship